Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

A synthetic aperture radar (SAR) target classification method has been developed, in the study, based on dynamic target reconstruction. According to SAR azimuthal sensitivity, the truly useful training samples for the reconstructing the test sample are those with approaching azimuths and same labels. Hence, the proposed method performs linear presentation of the test sample on the local dictionary established by several training samples selected from each class under the azimuthal correlation. By properly adjusting the azimuthal correlation constraint, the test sample can be reconstructed at different levels by different scales of training samples. During the classification phase, the reconstruction error vectors from different levels are combined by linear fusion and the label of the test sample is determined based on the fused errors. Experimental conditions are setup on the moving and stationary target acquisition and recognition (MSTAR) dataset to evaluate the proposed method. The results confirm the effectiveness of the proposed method.